Informatics Tools, AI Models and Methods Used for Automatic Analysis of Customer Satisfaction

Kovács, George; Bogdanova, Diana; Yussupova, Nafissa; Boyko, Maksym

doi:10.24846/v24i3y201503

Cited by 7 publications

(5 citation statements)

References 6 publications

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“…The ASAR manipulator is considered as a control object, the kinematic scheme of which is similar to the kinematic scheme of a human hand [15,16] and is shown in Figure 1. Similar manipulators are used in AR-600, FEDOR robots and are considered in detail in [17][18][19][20][21]. This manipulator has 7 rotational degrees of mobility.…”

Section: Methodsmentioning

confidence: 99%

Method of Controlling the Movement of an Anthropomorphic Manipulator in the Working Area With Dynamic Obstacle

Петренко¹,

Gurchinsky²,

Тебуева³

et al. 2020

Proceedings of the 8th Scientific Conference on Information Technologies for Intelligent Decision Making Support (ITIDS 2020)

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Currently, the rapidly developing direction of anthropomorphic robotics attracts great interest of developers. This is due to the need to perform routine, harmful and hazardous types of work without direct human intervention, which is the key to ensuring the safety of the tasks performed. The article discusses the issue of optimizing the trajectory of the manipulator when performing operations in the working area with an obstacle. To achieve this goal, an algorithm for controlling the movement of an anthropomorphic manipulator in a working area with dynamic obstacles is proposed using deep learning technology with amplification of a convolutional artificial neural network based on the DQN learning algorithm. This algorithm is more scalable than peers because it can be used for a wide variety of path planning problems in both deterministic and non-deterministic environments. The results of modeling the operation of a manipulator with seven rotational degrees of mobility in the working area with a typical obstacle in the form of a sphere are presented. The presented simulation results demonstrate the effectiveness of the proposed method and the need for its further development.

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Section: Methodsmentioning

confidence: 99%

Method of Controlling the Movement of an Anthropomorphic Manipulator in the Working Area With Dynamic Obstacle

Петренко¹,

Gurchinsky²,

Тебуева³

et al. 2020

Proceedings of the 8th Scientific Conference on Information Technologies for Intelligent Decision Making Support (ITIDS 2020)

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“…-Analytics software: to identify demand patterns, establish best reservation policy by market segment, optimize capacity utilization from predicted demand. The support to these decisions needs integrating in the OMS big data and AI techniques for market analysis, forecasting and sentiment analysis (Kovács et al, 2015); Radu-Florin Negoiță, Theodor Borangiu -Customer portal: to speed up capacity adjustment and keep customers up to date regarding service availability, delivery options and price incentives (SoftSuggest, 2022);…”

Section: Service Oms System Integratingmentioning

confidence: 99%

Robotic Process Automation of Inventory Demand with Intelligent Reservation

NEGOIŢĂ¹,

Borangiu²

2023

SIC

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The paper describes a solution that automates the inventory demand process for supporting facilities of a fixed service capacity, based on reservation with cost-effective overbooking strategy. This process, part of the back-office service management in the hospitality industry, is implemented in the robotic process automation (RPA) technology and integrated with front-office, customer interaction workflows in an operations management software (OMS) system. The main idea that led to this service model was the end-to-end (E2E) automation of the organisation, which is a new direction to merge AI techniques into one E2E platform with RPA capabilities at its basis: building, managing, and running software robots with process analytics tools to tackle a wider spectrum of business functions and knowledge intensive services with quick report of the business impact. Experimental results are reported for the RPA bot that establishes the overbooking strategy in the back-office.

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“…In this paper, we propose a solution to the problem of controlling two anthropomorphic manipulators based on an artificial neural network (ANN) and deep reinforcement learning. The use of ANN is due to neural networks showed high efficiency in solving problems that are difficult to solve analytically [20][21]. To achieve the result, we developed a training algorithm and structure of the control system for anthropomorphic manipulators.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Algorithm for Anthropomorphic Manipulator Control System

Петренко¹,

Тебуева²,

Svistunov³

et al. 2020

Proceedings of the 8th Scientific Conference on Information Technologies for Intelligent Decision Making Support (ITIDS 2020)

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Service robots are one of the relevant areas of modern robotics. Many service robots are equipped with a pair of anthropomorphic manipulators, so that they are able to perform complex operations. However, this approach leads to new challenges in development of the robot control systems. In this paper we propose an algorithm for training the control system of two anthropomorphic manipulators with 7 degrees of mobility having intersecting work areas. The algorithm is based on deep reinforcement learning approach applied to the artificial neural network (ANN). The paper also describes the practical implementation of the ANN-based manipulator control system that avoids collisions and achieves an average accuracy of reproducing target positions of manipulator end effector of 98.3%. The ANN training was carried out using Keras framework. The obtained results indicate the promise of applying the proposed method for the development of control systems for anthropomorphic manipulators based on deep reinforcement learning.

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Informatics Tools, AI Models and Methods Used for Automatic Analysis of Customer Satisfaction

Cited by 7 publications

References 6 publications

Method of Controlling the Movement of an Anthropomorphic Manipulator in the Working Area With Dynamic Obstacle

Method of Controlling the Movement of an Anthropomorphic Manipulator in the Working Area With Dynamic Obstacle

Robotic Process Automation of Inventory Demand with Intelligent Reservation

Machine Learning Algorithm for Anthropomorphic Manipulator Control System

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